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Learning through System Dynamics
as Preparation for the 21st Century
by
Jay W. Forrester
Germeshausen Professor Emeritus
and Senior Lecturer
Sloan School of Management
Massachusetts Institute of Technology
Keynote Address
for
Systems Thinking and Dynamic Modeling Conference
for K-12 Education
June 27-29, 1994
at
Concord Academy
Concord, MA, USA
Copyright © 1994
by Jay W. Forrester
Permission granted to reproduce
for noncommercial and educational purposes.
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Contents
DEVELOPING PERSONAL SKILLS
Basis for Clear Thought and Communication
Building Courage
Personal Philosophy
Seeing Interrelatedness
“Renaissance Man,” Unifying Knowledge, Mobility
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OUTLOOK AND PERSONALITY
Confidence in Creating the Future
Authoritarian vs. Innovative Personality
Mental Models and Computer Models
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UNDERSTANDING THE NATURE OF SYSTEMS
Cause and Effect Not Closely Related in Time or Space
Low-Leverage Policies
High Leverage Policies, Often Wrongly Applied
We Cause Our Own Problems
Drift to Low Performance, Collapse of Goals
Long-Term vs Short-Term Goals
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ACHIEVING THE BENEFITS OF A SYSTEMS EDUCATION
Experience and Participation
The Deeper Lessons
Systems Thinking vs System Dynamics
Revision of Road Maps
On Teaching Systems
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Learning through System Dynamics
as Preparation for the 21st Century
by
Jay W. Forrester
This conference emphasizes systems thinking and system dynamics
modeling. But what should such activities accomplish? We do not expect most
students to spend their lives in front of a computer building system dynamics
models. What should be the outcome of a systems education beyond the subjects
in this week’s program?
I believe we should give students a more effective way of interpreting the
world around them. They should gain a greater and well-founded confidence for
managing their lives and the situations they encounter.
The objectives of a system dynamics education might be grouped under
three headings:
1. Developing personal skills,
2. Shaping an outlook and personality to fit the 21st century, and
3. Understanding the nature of systems in which we work and live.
DEVELOPING PERSONAL SKILLS
A system dynamics education should sharpen clarity of thought and provide
a basis for improved communication. It should build courage for holding
unconventional opinions. It should instill a personal philosophy that is consistent
with the complex world in which we live.
Basis for Clear Thought and Communication
The ordinary spoken and written language allows a person to hide behind
ambiguous, incomplete, and even illogical statements. Language, within itself,
does not impose a discipline for clarity and consistency. By contrast, computer
modeling requires clear, rigorous statements.
In ordinary discussion, a general statement like, “How people respond
Copyright © 1994
by Jay W. Forrester
Permission granted to reproduce
for noncommercial and educational purposes.
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depends on the situation,” might be accepted. But, if this were to become an input
for a model, one would be forced to specify which people, what response,
dependence on what specific aspect of the situation, and what precise action would
to be taken under various conditions.
Students must struggle to achieve the precision of expression required to go
from language to explicit statements in a simulation model. Even a process as
simple as filling a bathtub with water, or describing the cooling of a cup of coffee,
can be surprisingly demanding. Such clarity is not achieved after only a few
exercises. Learning precision in thinking requires years of reinforcement.
Translating from descriptive language to model language is only half of the
story. One can then make the reverse translation. From a simulation model,
reverse translation to descriptive language yields clear statements that embody the
precision that came from building and using the model.
I experienced the power of reverse translation from a system dynamics
model after publication of my Urban Dynamics book dealing with the growth and
stagnation of cities (Forrester, 1969). The book achieved such visibility that I
would be invited to conferences on urban problems held anywhere in the world.
At such meetings, I had a unique power and influence derived from being able to
talk for 20 minutes without contradicting myself. Not contradicting oneself might
seem an ordinary competence. But others could not hope for comparable clarity
because of incompleteness and inconsistency in their thinking about complex
situations. Furthermore, they could not draw the correct dynamic consequences for
the future implied by the assumptions they were making. To know the behavior
that follows from assumptions about parts of a system can be achieved only
through modeling and computer simulation.
In my situation at the conferences on cities, I knew the assumptions that
went into the Urban Dynamics model. I knew the behavior that resulted from
those assumptions. Also, I knew how the behavior would change if one adopted
different political policies for guiding the evolution of a city. Within the
framework of the model, I could be entirely consistent in everything that I said. Of
course, one can be internally consistent and still be wrong or irrelevant. So,
beyond consistency, it was necessary that the model also overlay and connect with
the issues of interest to others.
Students should come out of a systems education convinced that a much
better understanding is possible in the present puzzling behavior of personal,
social, economic, and business situations. They should realize that any debate
about policies for the future can be clarified and made more meaningful if someone
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will make the underlying assumptions explicit and show which assumptions lead to
behavior that best fits the knowledge we have of the real world.
Students in kindergarten through 12th grade should have the repeated
experience of using modeling to resolve debates, misunderstandings, and
differences of opinion. One discovers that the most intense disagreements usually
arise, not because of differences about underlying assumptions, but from different
and incorrect intuitive solutions for the behavior implied by the assumptions. In
building a system dynamics model, one starts from the structure and the decisionmaking rules in a system. Usually there is little debate about structure and the
major considerations in decisions. When a model has been constructed from the
accepted structure and policies, the behavior will often be unexpected. As the
reasons for that behavior become understood, I have often seen extreme differences
of opinion converge into agreement.
Students should see modeling and an understanding of systems as a way to
reduce social and political conflict.
Building Courage
A strong background in modeling should show students that conventionally
accepted opinions about social and economic policies are often actually the causes
of our most serious problems. If they realize that popular opinions are not
necessarily correct, they should develop courage to think more deeply, look
beyond the immediate situation, and stand against majority opinion that is ill
founded and short sighted.
Working with models should not only enhance skill in making precise
statements, but also bolster the courage to do so. Very often people take refuge in
statements that are so general, so incomplete, and so superficial that they cannot be
proved wrong. On the other hand, such vague statements can not be effective.
Making precise statements opens one to being wrong. By a precise
statement I mean one that is unambiguous. A precise statement has a unique
meaning; it is clear. However, a precise statement is not necessarily accurate or
correct. Precise statements are necessary for clear communication. If such
statements are wrong, that will be more quickly discovered if communication is
clear. In model building, students will many times have the experience of making
assertions that model simulations demonstrate to be incorrect. Students should
develop the courage to be precise, even if wrong, in the process of learning and
improving understanding.
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Personal Philosophy
Experience in computer simulation should change the way students respond
to the world around them.
From simulation models, students should appreciate the complexity of social
and economic systems, whether those systems be at the level of families,
communities, corporations, nations, or international relationships. They should
have seen many times the counterintuitive nature of such systems. They should
understand that “obvious” solutions to problems are not always correct, and that
apparently correct actions are often the causes of the very problems that are being
addressed.
The Urban Dynamics book illustrates how well-meaning actions can worsen
conditions that the actions are intended to alleviate. The book showed how most
popular governmental policies all lay somewhere between neutral and highly
detrimental, either from the viewpoint of the city as an institution, or from the
viewpoint of unemployed low-income residents. The most powerful influence on a
city was shown to be the policy governing building of low-income housing. The
United States has followed a policy that makes urban poverty worse. As a city
ages, it becomes imbalanced. As industrial structures grow older, they are used in
ways that employ fewer people. However, as housing ages, it drifts to lower rents
and higher population densities. Building low-income housing accelerates the rate
of decay. The “obvious” policy of building low-cost housing occupies land that
could have been used for job-creating business structures while at the same time
the housing attracts still more people who need jobs. The apparently humanitarian
policy of building more housing actually creates poverty by pulling people into
areas of declining economic opportunity.
We can hope that students will develop caution about jumping to premature
conclusions and will search for a wider range of alternatives.
Even if individual students do not construct models in later life, they should
expect that system dynamics models will be constructed by those who are
proposing changes in economic and social policies. Moreover, in the 21st century,
citizens should expect that such models will be made available for public
inspection. From their K through 12th grade experiences they will know that they
can read, understand, and evaluate such models. More and more, computer models
will be used as the basis for determining social and economic policies. In order to
participate, the public will need to know the nature of such models, to evaluate the
assumptions in models, and to feel comfortable in pushing the proponents of policy
models to reveal their assumptions and to justify their conclusions.
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Seeing Interrelatedness
Interrelationships in systems are far more interesting and important than
separate details. The interrelationships reveal how the feedback loops are
organized that produce behavior. Students with a strong background in systems
modeling should be sensitized to the importance of how the world is organized.
They should want to search for interconnecting structure that gives meaning to the
parts.
One sees the significance of modeling in a discussion I had with a student
who had graduated from MIT several years before. I asked him what his system
dynamics study had done for him. His answer: “It gives me an entirely different
way of reading the newspapers.” He meant that he sees the relationships between
different things that are happening today, he understands the relationships between
today’s news and what happened last week and last year, and he reads between the
lines to know what must have been part of the story but was not reported.
“Renaissance Man,” Unifying Knowledge, Mobility
The 21st century will exhibit rapid changes in societies. We already see
turmoil in the former Soviet Union and in Africa. In the past century, change came
from new technologies. In the next century I believe change will be driven mostly
by population growth, crowding, environmental degradation, pollution, and
shortages of food, water, and resources. In other words, societies will be
continually reshaped and, as a consequence, the roles of individuals will
continually change. Today’s students should be prepared for unexpected change.
Education must reverse the trends of the last century toward more and more
specialization. A specialization interest can start early in life and lead to a
professional training in college that will often become obsolete within an
individual’s working career. Education should provide a foundation that gives a
student mobility to shift with changing demands and opportunities.
System dynamics provides a foundation underlying all subjects. When that
foundation is mastered, an individual will have mobility to move from field to
field. An MIT undergraduate in electrical engineering demonstrated such mobility.
He studied system dynamics during his junior and senior years. When he
continued for a master of science degree in electrical engineering, he did his thesis
on the way the body handles insulin and glucose in various aspects of diabetes.
That may not sound like electrical engineering, but about 10% of such students
move on to careers in medicine. He immediately developed a working-colleague
relationship with doctors in Boston’s research clinic for diabetes because for the
first time they were able to put together their fragments of medical knowledge into
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a meaningful system (Foster, 1970). But he did not intend to go into medicine. He
next worked with me in extending the Urban Dynamics model. For a year, he led
discussions with a group from Boston’s black community to incorporate many
aspects of education into the model. Later he went to work with a corporation. He
could move from one setting to another because his fundamental understanding of
systems allowed him to provide a dynamic organizing framework to any activity.
A person with an understanding of systems sees the common elements in
diverse settings rather than focusing on differences. For example, communities
may have identical basic structures but behave quite differently because of
different policies that are followed at crucial places. Systems with the same
structure show the same range of behaviors. For example, a simple two-level
model for a swinging pendulum can be relabeled and it becomes oscillating
employment and inventories at the core of economic business cycles.
I had an opportunity to illustrate transferability of structure in a discussion
with several medical doctors and pharmacologists. They had described an
experimental treatment that a research doctor was trying. Nothing had been said
about the results when I suggested, “Let me try something here. If I were to judge
that treatment by the behavior we saw in the Urban Dynamics model of growth
and decline in cities, I would predict that treatment would cause atrophy of the
pancreas.” And they responded, “You are right, that is exactly what happens.” We
expect that students should develop ability to transfer understanding of dynamic
structures among very different fields.
Transferability of structure and behavior should create a bridge between
science and the humanities. Feedback-loop structures are common to both. An
understanding of systems creates a common language. Science, economics, and
human behavior rest on the same kinds of dynamic structures.
I see a reversal of the trend toward specialization. As the underlying unity
between fields becomes teachable, we can move back toward that concept of the
“Renaissance Man,” who has broad intellectual interests and is accomplished in
areas of both the arts and the sciences.
OUTLOOK AND PERSONALITY
A systems education should give students confidence that they can shape
their own futures. A systems education should help mold a personality that looks
for causes and solutions. Working with systems should reveal the strengths and
weaknesses of mental models and show how mental models and computer models
can reinforce one another.
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Confidence in Creating the Future
Many of the stresses in modern life arise because people feel buffeted by
forces they neither understand nor know how to control. Such sense of
helplessness can be traced to not understanding the systems of which we are a part.
Events that seem capricious when viewed locally are often understandable when
seen from a broader systems perspective.
I hope that a system dynamics thread in K through 12 education would leave
individuals willing and able to appreciate the nature of complexity. They should
want to look beyond their immediate setting in search of the fundamental causes of
problems. They should develop optimism about understanding those problems of
society that earlier generations have found so baffling. Inflation, wars, unfavorable
balance of trade, and destruction of the environment have persisted for hundreds of
years without public understanding of the causes. Such problems are too serious to
be left to the self-appointed experts; the public must acquire the insights that
permit participation in debates of such importance.
Such better understanding comes in small steps. I am reminded of the story
told by a television producer who was taking video pictures in a group of parents,
teachers, and students at a school where the systems approach is making excellent
progress. The producer turned to a junior high school boy and asked, “What have
these systems studies meant to you?” His immediate answer: “I am much better
able to deal with my mother.”
Such ability to deal better with one’s environment starts with even very
simple systems. One of our MIT doctoral students in system dynamics went to
work for the Department of Energy. Two years later he told me he was amazed by
the amount of influence he could have on governmental thinking with a two-level
simulation model. Even such a simple system is often beyond what people in
important policy positions are taking into account.
Authoritarian vs. Innovative Personality
A systems education should mold the personality of students by enhancing
innovative tendencies in children and counteracting the forces in society that
convert an innovative personality into an authoritarian one. I am here using
authoritarian and innovative personalities in the sense described by Everett Hagen
in his book, On the Theory of Social Change (Hagen, 1962) Hagen contrasts two
opposite extremes of personality.
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The authoritarian personality fits into a rigid hierarchy. Life is capricious.
One does as ordered by those of higher status. There are no reasons for such
orders. Capricious orders fit the old army saying borrowed from Tennyson,
“Yours not to reason why, yours but to do and die.” The reward for yielding to
higher authority comes from the individual having authority over someone of
lower rank. The pure authoritarian personality expects no reasons for why things
happen and has no will to search for reasons.
By contrast, the innovative personality believes there are reasons for why
things happen. Even if the reasons are unknown, there is still the assumption that
reasons exist. Also, it is worth looking for the reasons because, if one understands,
then one can probably change and improve what is happening. The innovative
personality looks for causes and works toward beneficial advances.
I believe that babies are born as innovative personalities. They want to
explore, to understand, and to see how things work and how to master their
environments. But our social processes work to stamp out exploration and
questioning. The child is continually confronted with, “Do as you are told,” or
“Stop asking questions and just mind me,” or “Study this because it is good for
you.” Repeated restraint of innovative inclinations gradually forces personalities
into the authoritarian mold.
A system dynamics modeling curriculum, by letting students formulate the
structure and policies causing behavior under study, will help preserve and rebuild
the innovative outlook. Simulation emphasizes reasons for consequences. To be
innovative, one must be willing to make mistakes while searching for reasons and
improvement. Computer simulation modeling is a repeating process of trial and
error. One learns that progress is made through exploration and by learning from
mistakes. An authoritarian personality fears mistakes and does not try the
unknown. An innovative personality knows that mistakes are stepping stones to
better understanding.
Mental Models and Computer Models
Students should learn that all decisions are made on the basis of models.
Most models are in our heads. Mental models are not true and accurate images of
our surroundings, but are only sets of assumptions and observations gained from
experience.
Mental models control nearly all social and economic activities. Mental
models have great strengths, but also serious weaknesses. From a systems
education, students should learn how mental models can be useful and when they
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are unreliable. Furthermore, they should appreciate how computer simulation
models can compensate for weaknesses in mental models.
Mental models contain a vast wealth of information that is available no
where else. Mental models contain information about the structure and policies in
systems. By structure I mean the elements in a system and the connections
between the elements—who has what information, who is connected to whom,
and, what decisions are made and where. By policies I mean the rules that govern
decision making—what factors influence decisions, what is a particular decision
point trying to accomplish, and what goals are sought. At this detailed level of
structure and policies, mental models are rich and reasonably reliable sources of
information.
However, mental models have serious shortcomings. Partly, the weaknesses
in mental models arise from incompleteness, and internal contradictions. But more
serious is our mental inability to draw correct dynamic conclusions from the
structural and policy information in our mental models.
System dynamics computer simulation goes a long way toward
compensating for deficiencies in mental models. In model building, one must
remedy incompleteness and internal contradictions before the system dynamics
software will even allow simulation. After a logically complete model has been
created, one can be certain that the computer is correctly simulating the system
based on the assumptions that were incorporated in the model. It is in simulation,
or determining consequences of the structural and policy assumptions, that mental
models are unreliable, but computer models are completely dependable.
Students should also realize that there are no possible proofs of the validity
of any models, whether they are mental or computer models. Models are to be
judged by their comparative usefulness. Assumptions about structure and policies
should be compared with any available information. Computer simulation results
should be compared with behavior in the real system being represented.
Discrepancies lead to improving both mental and computer models.
A two-way street runs between mental models and computer models.
Mental models contribute much of the input for computer models. Creating a
computer model requires that the mental models be clarified, unified, and
extended. From the computer simulations come new insights about behavior that
give new meaning to mental models. Mental models will continue to be the basis
for most decisions, but those mental models can be made more relevant and more
useful by interacting with computer models.
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UNDERSTANDING THE NATURE OF SYSTEMS
We live in a network of complex systems. Yet few people realize the extent
to which those systems control human actions. In fact, few people realize the
extent to which complex systems actively mislead people into making
counterproductive decisions. Students, after a 12-year encounter with systems,
should be on guard against the deceptive nature of systems.
Cause and Effect Not Closely Related in Time or Space
Most understandable experiences teach us that cause and effect are closely
related in time and space. However, the idea that the cause of a symptom must lie
nearby and must have occurred shortly before the symptom is true only in simple
systems. In the more realistic complex systems, causes may be far removed in
both timing and location from their observed effects.
From earliest childhood we learn that cause and effect are closely associated.
If one touches a hot stove, the hand is burned here and now. When one stumbles
over a threshold, the cause is immediately seen as not picking the foot high
enough, and the resulting fall is immediate. All simple feedback processes that we
fully understand reinforce the same lesson of close association of cause and effect.
However, those lessons are aggressively misleading in more complex systems.
In systems composed of many interacting feedback loops and long time
delays, causes of an observed symptom may come from an entirely different part
of the system and lie far back in time.
To make matters even more misleading, such systems present the kind of
evidence that one has been conditioned to expect. There will be apparent causes
that meet the test of being closely associated in time and in location. However,
those apparent causes are usually coincident symptoms arising from the distant
cause. People are thereby drawn to actions that are not relevant to the problem at
hand.
Comments such as I have just made about cause and effect carry little
conviction from being stated in a lecture. Only after a student has repeatedly
worked with models that demonstrate such behavior, and has had time to observe
the same kinds of behavior in real life, will the idea be internalized and become
part of normal thinking.
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Low-Leverage Policies
Complex systems differ from simple systems in another way. In simple
systems, the policies to yield better results are obvious and they work. To avoid
burning your fingers on a hot stove, you keep away from the stove. But in
complex systems, the apparently influential policies often have very little effect.
When I talk to a group of business executives I ask how many have ever had
the experience of facing a serious problem, devising policies to correct the
situation, and five years later find there has been no improvement. Most will hold
up their hands. Perhaps you have experienced the same in education. The quality
of education has been severely criticized, many educators have tried remedies, and
often there is little change.
In complex systems, there are many interconnecting feedback loops. A new
policy, which is intended to solve a problem, causes reactions in other parts of the
system that counteract the new policy. In education that reaction may come from
administrators, from school boards, from parents who do not want new
experimental ideas tried on their children, or from budget pressures.
I believe that a very high percentage, say 98%, of the policies in a system
have very little leverage to create change. They do not matter. However, most of
the heated debates in communities, companies, and governments are about policies
that are not influential. Such debates are a waste of time and energy. Debates
about low-leverage policies divert attention from the few policies that could lead to
improvement.
Students must have experience working with models of complex systems to
appreciate how often proposed policies fail to produce results.
High Leverage Policies, Often Wrongly Applied
Fortunately, a few high-leverage policies exist that can alter the behavior of
a system. However, high-leverage policies lay another trap for the unwary. One
occasionally finds a person who is working with a high-leverage policy. However,
I estimate that more than 90% of the time that person is pushing the high-leverage
policy in the opposite direction relative to what that person wants to accomplish.
In complicated systems, intuition provides no reliable guide even to the direction
that a high-leverage policy should be changed.
I have several times had the experience of going into a company with a
serious difficulty where intended policies were causing the problem. We are
talking here of a highly visible problem. It might be low profitability, or falling
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market share, or severe instability with the company working overtime one year
and having half the people laid off two years later. One carries on extensive
interviews to determine the policies (decision-making rules) that people are using
in different positions in the company. People justify their policies as intended to
solve the major problem. One then puts the expressed policies into a system
dynamics simulation model and finds that the model generates the same difficulty
that the company is experiencing. In other words, the policies that people know
they are following are the cause of their trouble. Local interpretation of symptoms
leads to local actions that combine to produce detrimental results. This is a
treacherous situation. If people believe their actions will reduce the problem, but
do not know those actions are making it worse, then as matters become worse there
is growing incentive to take the presumed corrections that are actually causing
further decline.
One sees this spiral of system deterioration at all levels in society.
Individuals in a family in serious psychiatric difficulty know they are in trouble,
each wants to do something to help, yet everything that everyone does makes
matters worse. In the Urban Dynamics model, we saw that governmental policies
about low-cost housing do not improve cities but cause more decay. In the same
way, we might suspect that our national foreign trade policies lead to importing
goods made by low-skilled labor while our own low-skilled population lose the
jobs that could provide an employment and training ladder to higher skills.
I do not know of any way to determine which are high-leverage policies and
which direction to apply them except to do a systems dynamics simulation of the
situation. Students should have many experiences working with models that reveal
the multitude of policies having little effect, that allow them to search for highleverage policies, and that show them the danger of intuitively judging even the
direction of effect of high-leverage policies. Students should come out of a
systems education with an appreciation for how mental models alone can lead one
astray in multiple-loop systems. They should demand that important issues be
modeled, and that the models be made available to the public. They should have
confidence that they can read and evaluate such models. Models then become a
powerful and explicit means of communication.
We Cause Our Own Problems
The often quoted line from the comic strips, “We have met the enemy, and
he is us,” has more than a grain of truth. Usually, problems exhibited by a social
system are caused by the people in that system. However, people naturally tend to
blame others. When Detroit was losing market share to Japanese automobiles,
executives of American companies blamed Japan for dumping at low prices, when
the real cause was Detroit’s own declining quality. Parents blame schools for low
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competence of students, when perhaps the deficiency arises more from preschool
home life and failure in parental guidance. A company is more inclined to blame
falling sales on unfair competition or fickle consumers than on its own poor
products and service.
In preparation for the 21st century, a systems education should condition
students to look for the source of their troubles first in their own actions before
blaming others.
Drift to Low Performance, Collapse of Goals
One component of any feedback loop is the goal toward which the feedback
process is striving. In simple models, goals are usually given as constants, for
example, the goal of a pendulum is to seek the vertical as it swings from one side
to the other. The goal of an inventory manager may be to maintain a given level of
inventory, The goal that determines the amount of sleep we get is to maintain a
certain degree of restfulness. But in a more complete representation of systems,
the goals themselves are properly shown as variables. We may be striving toward
a certain goal, but, failing to reach the goal, we may readjust our goal to something
that seems more achievable.
There is a strong tendency for goals of all kinds—personal, community,
corporate, or national—to drift downward. Pressures tend to cause performance to
fall short of goals. But failing to meet goals is uncomfortable. The response is
often to let the goals adjust downward toward the actual performance. As goals
fall, the incentives for high achievement decline. Performance continues to fall
short of the new lower goals and the downward spiral continues.
Falling goals will in time lead to crisis, but by then recovery may be
impossible. One sees erosion of goals in attitudes toward the national deficit.
Thirty years ago, the present size of the national deficit would have been
unthinkable. But as the deficit rose, people came to accept each new rise and
adjusted to the higher deficit. Eventually such goal erosion can lead to disaster.
Successful people, successful corporations, and successful countries have
leadership or deeply held beliefs that stop such goal erosion.
Students should be exposed to the dynamics of goal collapse in models and
have an opportunity to relate the process to their own lives. Goal collapse, that is,
becoming accustomed to and accepting falling standards, may be the greatest threat
to the future of individuals and countries.
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Long-Term vs Short-Term Goals
A fundamental conflict exists between short-term and long-term goals.
Students should observe this conflict between the present and the future in system
dynamics models and then relate the lessons to their own lives. Actions that yield
immediate rewards almost always exact punishment in the long run, and vice
versa. Quick gratification is the enemy of future well-being. It is hard to find
exceptions where actions with an immediate reward do not extract a price in the
more distant future.
A person who steals may benefit immediately, but usually suffers later. A
person who works all night to finish an important task pays by being inefficient for
the next several days. Taking mind-altering drugs may give an immediate sense of
well being at the expense of future ill health or poverty. Borrowing on credit cards
allows an immediate increase in standard of living but the consequence in the
longer term is a lower standard of living while paying back the loan and interest.
Under pressure from voters, the U.S. Congress is borrowing money to provide
ever-increasing goodies to constituents, with the probable future consequence that
government becomes insolvent and may not be able to provide basic public
services. Over a much longer time horizon, improved public health and modern
agriculture raised the standard of living and reduced death rates, resulting now in
the threat of an unsustainable population explosion.
Conversely, accepting a short-term disadvantage can often yield rewards in
the longer-term. For example, saving now, rather than spending all one’s income,
can increase the future standard of living. A company that foregoes higher
dividends and increased executive salaries can invest in research on new products
and increase future income.
The conflict between short-term and long-term goals bears directly on what
should be considered ethical and humanitarian. Humanitarian impulses are usually
based on short-term considerations but often lead to worsening the situation in the
more distant future. Food aid to starving populations seems humanitarian in the
short run, but may well encourage population growth and greater starvation of even
more people in the future.
Students should study the fundamental conflicts between short-term and
long-term goals in the context of system dynamics models and have the
opportunity to relate the lessons to their families, communities, and nation.
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ACHIEVING THE BENEFITS OF A SYSTEMS EDUCATION
A systems thinking and systems modeling curriculum will not automatically
yield the lifetime insights and personal guidance that I have been discussing. A
student might easily go through the motions of working with models without
gaining the understanding that is potentially available.
Experience and Participation
Students will not internalize their understanding of systems merely from
being told. Nor will discussion and debate be effective. Coming to an
understanding of systems must be a participative activity. Learning about systems
in not a spectator sport, such learning comes from active involvement. One does
not learn to ride a bicycle or play basketball from lectures alone; one must practice.
A person learns from experience. Computer modeling allows an accelerated
vicarious experience.
The Deeper Lessons
A student can work with computer simulation models without realizing the
deeper lessons that should be absorbed. Most learning for the 21st century that I
have discussed can be missed by students unless the right guidance is provided.
Students must create their own models and learn from trial and error. They must
be led toward models that can teach the lessons that I have been discussing. Even
with models that contain the lessons, students can miss the most important
implications, so they should be encouraged to see the deeper consequences of what
they are doing. They should relate what they are learning to systems they already
know in families, community, and school.
Systems Thinking vs System Dynamics
This conference is advertised as “Systems Thinking and Dynamic
Modeling.” Consider those two activities in the context of learning for the 21st
century. I understand and define the two terms, systems thinking, and dynamic
modeling, to mean quite different activities.
Systems thinking appears to be thinking about systems, talking about the
characteristics of systems, acknowledging that systems are important, discussing
some of the insights from system archetypes, and relating the experiences people
have with systems. Systems thinking is lecturing about systems as I am doing
tonight. Systems thinking can be a door opener and a source of incentive to go
deeper into the study of systems. But I believe that systems thinking has almost no
chance of instilling the lessons that I have described. Systems thinking will
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change very few of the mental models that students will use in their future decision
making. Systems thinking is not more than five percent of a systems education.
On the other hand, system dynamics modeling is learning by doing. It is
learning through being surprised by the mistakes one makes. System dynamics
modeling is a participative activity in which one learns by trial and error and
practice. I believe that immersion in such active learning can change mental
models.
Revision of Road Maps
Many of you are already familiar with the Road Maps series being written
by undergraduate students at MIT with my guidance. Road Maps are documents
intended as a self-study guide to learning about systems. First drafts of four
chapters have been available through the Creative Learning Exchange.1
However, in thinking about lessons that students should learn, it became
clear that intended insights about systems could be missed. Work with systems
was there, but implications had not been stressed. We are now revising the early
drafts of Road Maps to be more explicit about fundamental principals of systems
and to call attention to the general characteristics of systems that should be
observed. The series is also being extended to more comprehensive chapters.
On Teaching Systems
I believe that confining student learning to systems thinking and to
discussion about systems will convey very little understanding of the nature and
behavior of the systems within which we live.
To appreciate the nature of systems, students must have extensive personal
experience in working with systems. This means creating system dynamics
models on a computer, simulating their behavior, exploring how the models
respond to changes in structure and policies, and comparing model behavior to the
real systems being represented. Such active modeling should extend at least
throughout the several years of middle school and high school. As early as
possible, schools should move away from canned models that have been
previously prepared for student use. Instead, students should create models,
examine their short comings, and learn from discovering improvements.
1
Creative Learning Exchange, Lees Stuntz, Executive director, 1 Keefe Road, Acton, MA,
01720, tel: 508-287-0070, fax: 508-287-0080.
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Students should gain experience in modeling systems in which they have a
personal interest. Such systems can be drawn from family and community
situations. Items from the newspapers should be converted to formal models to
reveal student understanding of current events, to detect omissions and
contradictions in the news items, and to provide practice in moving in both
directions between mental and computer models. History and literature likewise
provide material that can be made more explicit and understandable through
modeling.
Throughout student work with models, more should be learned than just the
details of the models themselves. Beneath such models are the underlying
principles of systems (Forrester, 1968). Beyond such models are the kinds of
learning discussed in this talk. Students probably will not see such general and
transferable insights merely from exposure to models. The larger and more
enduring lessons must be pointed out. Students should work with examples of the
broader implications. Such active use of the insights will thereby become part of
their thinking and the way they look at the world around them.
For Further Reading
Alfeld, Louis Edward, and Alan K. Graham. 1976. Introduction to Urban Dynamics. Portland,
OR: Productivity Press. 333 pp.
Forrester, Jay W. 1961. Industrial Dynamics. Portland, OR: Productivity Press. 464 pp.
Forrester, Jay W. 1968. Principles of Systems. (2nd ed.). Portland, OR: Productivity Press. 391
pp.
Forrester, Jay W. 1969. Urban Dynamics. Portland, OR: Productivity Press. 285 pp.
Forrester, Jay W. 1971. World Dynamics. (1973 second ed.). Portland, OR: Productivity Press.
144 pp. Second edition has an added chapter on physical vs. social limits.
Forrester, Jay W. 1975. Collected Papers of Jay W. Forrester. Portland, OR: Productivity Press.
284 pp.
Forrester, Nathan B. 1973. The Life Cycle of Economic Development. Portland, OR: Productivity
Press. 194 pp.
Foster, Richard O. (1970). The Dynamics of Blood-Sugar Regulation. M.Sc. thesis, M. I. T.
Goodman, Michael R. 1974. Study Notes in System Dynamics. Portland, OR: Productivity Press.
388 pp.
Hagen, Everett E. (1962). On the Theory of Social Change. Homewood, Illinois: Dorsey Press.
557 pp.
Lyneis, James M. 1980. Corporate Planning and Policy Design: A System Dynamics Approach.
Portland, OR: Productivity Press. 520 pp.
Mass, Nathaniel J., ed., 1974. Readings in Urban Dynamics: Volume I, Portland, OR:
Productivity Press, 303 pp.
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Mass, Nathaniel J. 1975. Economic Cycles: An Analysis of Underlying Causes. Portland, OR:
Productivity Press. 185 pp.
Meadows, Dennis L. 1970. Dynamics of Commodity Production Cycles. Portland, OR:
Productivity Press. 104 pp.
Meadows, Dennis L., et al. 1974. Dynamics of Growth in a Finite World. Portland, OR:
Productivity Press. 637 pp.
Meadows, Dennis L., and Donella H. Meadows, ed., 1973. Toward Global Equilibrium:
Collected Papers, Portland, OR: Productivity Press, 358 pp.
Randers, Jorgen, ed., 1980. Elements of the System Dynamics Method, Portland, OR:
Productivity Press, 488 pp.
Richardson, George P., and Alexander L. Pugh III. 1981. Introduction to System Dynamics
Modeling with DYNAMO. Portland, OR: Productivity Press. 413 pp.
Roberts, Edward B. 1978. Managerial Applications of System Dynamics. Portland, OR:
Productivity Press. 562 pp.
Roberts, Nancy, David Andersen, Ralph Deal, Michael Garet, William Shaffer. 1983.
Introduction to Computer Simulation: A System Dynamics Modeling Approach. Portland
OR: Productivity Press, 562 pages.
Schroeder, Walter W., III, Robert E. Sweeney, and Louis Edward Alfeld, ed., 1975. Readings in
Urban Dynamics: Volume 2, Portland, OR: Productivity Press, 305 pp.